Prediction Using Classification and Regression

Classification algorithms return accurate predictions based on our observations. Starting from a set of predefined class labels, the classifier assigns each piece of input data a class label according to the training model. Classification algorithms learn linear or non-linear associations between independent and categorical dependent variables. For example, a classification algorithm may learn to predict the weather as clear sky, gentle showers or heavy rain, and so on. Regression relates a set of independent variables to a dependent variable, numeric or continuous, for example, predicting rainfall in units of millimeters. Through this technique, it is possible to understand how the value of the dependent variable changes as the independent variable varies. This chapter shows us how to classify an object using nearest neighbors and how to perform an accurate regression analysis in a MATLAB environment. The aim of this chapter is to provide...